Google Gemini has the worst LLM API
191 comments
·May 3, 2025simonw
anaisbetts
It's a way for you to have your AI billing under the same invoice as all of your other cloud purchases. If you're a startup this is a dumb feature, if you work at a $ENTERPRISE_BIGCO, it just saved you 6mo+ of fighting with IT / Legal / various annoying middle managers
blitzar
> $ENTERPRISE_BIGCO, it just saved you 6mo+ of fighting with IT / Legal / various annoying middle managers
What's the point of working at $ENTERPRISE_BIGCO if you don't fight with IT & Legal & various annoying middle managers.
Anyway let's table this for now and circle back later after we take care of some of the low hanging fruit. Keep me in the loop and I will do a deep dive into how we can think outside the box and turn this into a win-win. I will touch base with you when I have all my ducks in a row and we can hop on a call.
kridsdale1
I work AT Google and 99% of my conversations must have been the training set for your paragraph.
progbits
It's also useful in a startup, I just start using it with zero effort.
For external service I have to get a unique card for billing and then upload monthly receipts, or ask our ops to get it setup and then wait for weeks as the sales/legal/compliance teams on each side talk to each other.
NoahZuniga
This is not true??? The AI studio surface is also billed on a per project basis?
bn-l
ah! thank you. I was also struggling with where vertex fitted.
tzury
Vertex by example:
creds = service_account.Credentials.from_service_account_file(
SA_FILE,
scopes=[
"https://www.googleapis.com/auth/cloud-platform",
"https://www.googleapis.com/auth/generative-language",
]
)
google.genai.Client(
vertexai=True,
project=PROJECT_ID,
location=LOCATION,
http_options={"api_version": "v1beta1"},
credentials=sa_creds,
)
That `vertexai=True` does the trick - you can use same code without this option, and you will not be using "Vertex".Also, note, with Vertex, I am providing service account rather than API key, which should improve security and performance.
For me, the main aspect of "using Vertex", as in this example is the fact Start AI Cloud Credit ($350K) are only useable under Vertex. That is, one must use this platform to benefit from this generous credit.
Feels like the "Anthos" days for me, when Google now pushing their Enterprise Grade ML Ops platform, but all in all I am grateful for their generosity and the great Gemini model.
sitefail1
I don't think a service account vs an API key would improve performance in any meaningful way. I doubt the AI endpoint is authenticating the API key against a central database every request, it will most certainly be cached against a service key in the same AZ or whatever GCP call it.
ivanvanderbyl
Service account file vs API Key have similar security risks if provided the way you are using them. Google recommends using ADC and it’s actually an org policy recommendation to disable SA files.
wanderer2323
ADC (Application Default Credentials) is a specification for finding credentials (1. look here 2. look there etc.) not an alternative for credentials. Using ADC one can e.g. find an SA file.
As a replacement for SA files one can have e.g. user accounts using SA impersonation, external identity providers, or run on GCP VM or GKE and use built-in identities.
(ref: https://cloud.google.com/iam/docs/migrate-from-service-accou...)
logankilpatrick
The startup credits are fully compatible with AI Studio, they are not specific to Vertex.
laborcontract
Google Cloud Console's billing console for Vertex is so poor. I'm trying to figure out how much i spent on which models and I still cannot for the life of me figure it out. I'm assuming the only way to do it is to use the gemini billing assistant chatbot, but that requires me to turn on another api permission.
I still don't understand the distinction between Gemini and Vertex AI apis. It's like Logan K heard the criticisms about the API and helped push to split Gemini from the broader Google API ecosystem but it's only created more confusion, for me at least.
chrisheecho
I couldn’t have said it better. My billing friends are working to address some of these concerns along with the Vertex team. We are planning to address this issue. Please stay tuned, we will come back to this thread to announce when we can In fact, if you can DM me (@chrischo_pm on X) with, I would love to learn more if you are interested.
jeswin
Can you allow prepaid credits as well please?
tyre
Gemini’s is no better. Their data can be up to 24h stale and you can’t set hard caps on API keys. The best you can do is email notification billing alerts, which they acknowledge can be hours late.
__jl__
Only problem is that the genai API at https://ai.google.dev is far less reliable and can be problematic for production use cases. Right around the time Gemini 2.0 launched, it was done for days on end without any communication. They are putting a lot of effort into improving it but it's much less reliable than openai, which matters for production. They can also reject your request based on overall system load (not your individual limits), which is very unpredictable. They advertise 2000 requests per minute. When I tried several weeks ago, I couldn't even get 500 per minute.
logankilpatrick
Pls ping me if you run into any production issues, will raise right away to the team. We have massive at scale products operating on AI Studio, so we are set up to ensure stability.
mgraczyk
OpenAI compatible API is missing important parameters, for example I don't think there is a way to disable flash 2 thinking with it.
Vertex AI is for grpc, service auth, and region control (amongst other things). Ensuring data remains in a specific region, allowing you to auth with the instance service account, and slightly better latency and ttft
simonw
I find Google's service auth SO hard to figure out. I've been meaning to solve deploying to Cloud Run via service with for several years now but it just doesn't fit in my brain well enough for me to make the switch.
chrisheecho
simonw, 'Google's service auth SO hard to figure out' – absolutely hear you. We're taking this feedback on auth complexity seriously. We have a new Vertex express mode in Preview (https://cloud.google.com/vertex-ai/generative-ai/docs/start/... , not ready for primetime yet!) that you can sign up for a free tier and get API Key right away. We are improving the experience, again if you would like to give feedback, please DM me on @chrischo_pm on X.
mgraczyk
If you're on cloud run it should just work automatically.
For deploying, on GitHub I just use a special service account for CI/CD and put the json payload in an environment secret like an API key. The only extra thing is that you need to copy it to the filesystem for some things to work, usually a file named google_application_credentials.json
If you use cloud build you shouldn't need to do anything
PantaloonFlames
You could post on Reddit asking for help and someone is likely to provide answers, an explanation, probably even some code or bash commands to illustrate.
And even if you don't ask, there are many examples. But I feel ya. The right example to fit your need is hard to find.
mountainriver
GCP auth is terrible in general. This is something aws did well
minimaxir
From the linked docs:
> If you want to disable thinking, you can set the reasoning effort to "none".
For other APIs, you can set the thinking tokens to 0 and that also works.
mgraczyk
Wow thanks I did not know
chrisheecho
We built the OpenAI Compatible API (https://cloud.google.com/vertex-ai/generative-ai/docs/multim...) layer to help customers that are already using OAI library to test out Gemini easily with basic inference but not as a replacement library for the genai sdk (https://github.com/googleapis/python-genai). We recommend using th genai SDK for working with Gemini.
mike_hearn
So, to be clear, Google only supports Python as a language for accessing your models? Nothing else?
logankilpatrick
This is documented for AI Studio here: https://ai.google.dev/gemini-api/docs/openai#thinking
Aeolun
When I used the openai compatible stuff my API’s just didn’t work at all. I switched back to direct HTTP calls, which seems to be the only thing that works…
franze
yeah, 2 days to get Google OAuth flow integrated into an background app/script, 1 day coding for the actual app ...
jpc0
Is this vertexAI related or in general, I find googles oauth flow to be extremely well documented and easy to setup…
jacob019
I got claude to write me an auth layer using only python http.client and cryptography. One shot no problem, now I can get a token from the service key any time, just have to track expiration. Annoying that they don't follow industry standard though.
arccy
should have used ai to write the integrations...
shresbm123
We support reasoning_effort = none. That will let you disable flash 2 thinking. We will document it better.
omneity
JSONSchema support on Google's OpenAI-compatible API is very lackluster and limiting. My biggest gripe really.
shresbm123
yeah we are looking into it
chrisheecho
simonw, good points. The Vertex vs. non-Vertex Gemini API (via AI Studio at aistudio.google.com) could use more clarity.
For folks just wanting to get started quickly with Gemini models without the broader platform capabilities of Google Cloud, AI Studio and its associated APIs are recommended as you noted.
However, if you anticipate your use case to grow and scale 10-1000x in production, Vertex would be a worthwhile investment.
troupo
Why create two different APIs that are the same, but only subtly different, and have several different SDKs?
chrisheecho
I think you are talking about generativeai vs. vertexai vs. genai sdk.
And you are watching us evolve overtime to do better.
Couple clarifications 1. Going forward we only recommend using genai SDK 2. Subtle API differences - this is a bit harder to articulate but we are working to improve this. Please dm at @chrischo_pm if you would like to discuss further :)
unknown_user_84
Indeed. Though the billing dashboard feels like an over engineered April fool's joke compared to Anthropic or OpenAI. And it takes too long to update with usage. I understand they tacked it into GCP, but if they're making those devs work 60 hours a week can we get a nicer, and real time, dashboard out of it at least?
logankilpatrick
we will have a dashboard in AI Studio very soon! Then will work to drive down delay.
coredog64
Wait until you see how to check Bedrock usage in AWS.
(While you can certainly try to use CloudWatch, it’s not exact. Your other options are “Wait for the bill” or log all Bedrock invocations to CloudWatch/S3 and aggregate there)
jacob019
Except that the OpenAI compatible endpoint isn't actually compatible. Doesn't support string enum values for function calls and throws a confusing error. Vertex at least has better error messages. My solution, just use text completions and emulate the tool call support client side, validate the responses against the schema, and retry on failure. It rarely has to retry and always works the 2nd time even without feedback.
ashu1461
There is also no way to over-write content moderation settings, and half of the responses you generate via open ai endpoint end up being moderated.
chrisheecho
Hey there, I’m Chris Cho (x: chrischo_pm, Vertex PM focusing on DevEx) and Ivan Nardini (x: ivnardini, DevRel). We heard you and let us answer your questions directly as possible.
First of all, thank you for your sentiment for our latest 2.5 Gemini model. We are so glad that you find the models useful! We really appreciate this thread and everyone for the feedback on Gemini/Vertex
We read through all your comments. And YES, – clearly, we've got some friction in the DevEx. This stuff is super valuable, helps me to prioritize. Our goal is to listen, gather your insights, offer clarity, and point to potential solutions or workarounds.
I’m going to respond to some of the comments given here directly on the thread
ctxc
Had to move away from Gemini because the SDK just didn't work.
Regardless of if I passed a role or not, the function would say something to the effect of "invalid role, accepted are user and model".
Tried switching to openAI compatible SDK, it threw errors for tool call calls and I just gave up.
Could you confirm if it was a known bug that was fixed?
ctxc
chrisheecho
You don't have to specify role when you call through Python (https://cloud.google.com/vertex-ai/generative-ai/docs/start/...)
(which I think is what you are using but maybe i'm wrong).
Feel free to DM me on @chrischo_pm on X. Stuff that you are describing shouldn't happen
Deathmax
Can we avoid weekend changes to the API? I know it's all non-GA, but having `includeThoughts` suddenly work at ~10AM UTC on a Sunday and the raw thoughts being returned after they were removed is nice, but disruptive.
chrisheecho
Can you tell me the exact instance when this happened please? I will take this feedback back to my colleagues. But in order to change how we behave I need a baseline and data
Deathmax
Thoughts used to be available in the Gemini/Vertex APIs when Gemini 2.0 Flash Thinking Experimental was initially introduced [1][2], and subsequently disabled to the public (I assume hidden behind a visibility flag) shortly after DeepSeek R1's release [3] regardless of the `include_thoughts` setting.
At ~10:15AM UTC 04 May, a change was rolled out to the Vertex API (but not the Gemini API) that caused the API to respect the `include_thoughts` setting and return the thoughts. For consumers that don't handle the thoughts correctly and had specified `include_thoughts = true`, the thinking traces then leaked into responses.
[1]: https://googleapis.github.io/python-genai/genai.html#genai.t...
[2]: https://ai.google.dev/api/generate-content#ThinkingConfig
[3]: https://github.com/googleapis/python-genai/blob/157b16b8df40...
jbellis
Can you ask whoever owns dashboards to make it so I can troubleshoot quota exceeded errors like this? https://x.com/spyced/status/1917635135840858157
logankilpatrick
We are working on fixing this and showing the critical ones in AIS. I agree it is crazy there is 700+ items here. Real pain in the neck to deal with.
egamirorrim
I love that you're responding on HN, thanks for that! While you're here I don't suppose you can tell me when Gemini 2.5 Pro is hitting European regions on Vertex? My org forbids me from using it until then.
m3adow
Yeah, not having clear time lines for new releases on the one hand, but being quick with deprecation of older models isn't a very good experience.
froggertoaster
Thanks for replying, and I can safely say that most of us just want first-class conformity with OpenAI's API without JSON schema weirdness (not using refs, for instance) baked in.
troupo
Or returning null for null values, not some "undefined" string.
Or not failing when passing `additionalProperties: false`
Or..
irthomasthomas
Hi, one thing I am really struggling with in AI studio API is stop_sequences. I know how to request them, but cannot see how to determine which stop_sequence was triggered. They don't show up in the stop_reason like most other APIs. Is that something which vertex API can do? I've built some automation tools around stop_sequences, using them for control logic, but I can't use Gemini as the controller without a lot of brittle parsing logic.
shresbm123
Thank you feedback noted
troupo
Is there an undocumented hardcoded timeout for Gemini responses even in streaming mode? JSON output according to a schema can get quite lengthy, and I can't seem to get all of it for some inputs because Gemini seemingly terminates requests
NoahZuniga
This is probably just you hitting the model's internal output length maximum. Its 65,536 tokens for 2.5 pro and flash.
For other models, see this link and open up the collapsed section for your specific model: https://ai.google.dev/gemini-api/docs/models
troupo
Thanks! It might just be that!
null
asadm
I don't get the outrage. Just use their OpenAI endpoints: https://ai.google.dev/gemini-api/docs/openai
It's the best model out there.
ramoz
I have no issues with their native structured outputs either. Other than confusing and partially incomplete documentation.
chrisheecho
Ramoz, good to hear that native Structured Outputs are working! But if the docs are 'confusing and partially incomplete,' that’s not a good DevEx. Good docs are non-negotiable. We are in the process of revamping the whole documentation site. Stay tuned, you will see something better than what we have today.
ramoz
Product idea for structured outputs: Dynamic Json field... like imagine if I want a custom schema generated (e.g. for new on-the-fly structured outputs).
malshe
Thanks for sharing this. I did not know this existed
rafram
Site seems to be down - I can’t get the article to load - but by far the most maddening part of Vertex AI is the way it deals with multimodal inputs. You can’t just attach an image to your request. You have to use their file manager to upload the file, then make sure it gets deleted once you’re done.
That would all still be OK-ish except that their JS library only accepts a local path, which it then attempts to read using the Node `fs` API. Serverless? Better figure out how to shim `fs`!
It would be trivial to accept standard JS buffers. But it’s not clear that anyone at Google cares enough about this crappy API to fix it.
chrisheecho
That’s correct! You can send images through uploading either the Files API from Gemini API or Google Cloud Storage (GCS) bucket reference. What we DON’T have a sample on is sending images through bytes. Here is a screenshot of the code sample from the “Get Code” function in the Vertex AI studio. https://drive.google.com/file/d/1rQRyS4ztJmVgL2ZW35NXY0TW-S0... Let me create a feature request to get these samples in our docs because I could not find a sample too. Fixing it
Deathmax
> You can’t just attach an image to your request.
You can? Google limits HTTP requests to 20MB, but both the Gemini API and Vertex AI API support embedded base64-encoded files and public URLs. The Gemini API supports attaching files that are uploaded to their Files API, and the Vertex AI API supports files uploaded to Google Cloud Storage.
rafram
Their JavaScript library didn’t support that as of whenever I tried.
simonw
I got their most recent JavaScript API library to work for images here: https://tools.simonwillison.net/gemini-mask
Here's the code: https://github.com/simonw/tools/blob/main/gemini-mask.html
mofunnyman
Semi hugged.
ryao
I have not pushed my local commits to GitHub lately (and probably should), but my experience with the Gemini API so far has been relatively positive:
https://github.com/ryao/gemini-chat
The main thing I do not like is that token counting is rated limited. My local offline copies have stripped out the token counting since I found that the service becomes unusable if you get anywhere near the token limits, so there is no point in trimming the history to make it fit. Another thing I found is that I prefer to use the REST API directly rather than their Python wrapper.
Also, that comment about 500 errors is obsolete. I will fix it when I do new pushes.
yorick
It looks like you can use the gemma tokenizer to count tokens up to at least the 1.5 models. The docs claim that there's a local compute_tokens function in google-genai, but it looks like it just does an API call.
Example for 1.5:
https://github.com/googleapis/python-aiplatform/blob/main/ve...
lemming
Additionally, there's no OpenAPI spec, so you have to generate one from their protobuf specs if you want to use that to generate a client model. Their protobuf specs live in a repo at https://github.com/googleapis/googleapis/tree/master/google/.... Now you might think that v1 would be the latest there, but you would be wrong - everyone uses v1beta (not v1, not v1alpha, not v1beta3) for reasons that are completely unclear. Additionally, this repo is frequently not up to date with the actual API (it took them ages to get the new thinking config added, for example, and their usage fields were out of date for the longest time). It's really frustrating.
chrisheecho
lemming, this is super helpful, thank you. We provide the genai SDK (https://github.com/googleapis/python-genai) to reduce the learning curve in 4 languages (GA: Python, Go Preview: Node.JS, Java). The SDK works for all Gemini APIs provided by Google AI Studio (https://ai.google.dev/) and Vertex AI.
egamirorrim
The way dependency resolution works in Java with the special, Google only, giant dynamic BOM resolver is hell on earth.
We have to write code that round robins every region on retries to get past how overloaded/poorly managed vertex is (we're not hitting our quotas) and yes that's even with retry settings on the SDK.
Read timeouts aren't configurable on the Vertex SDK.
ezekiel68
Eh, you know. "Move fast and break things."
caturopath
I'm not sure "move fast" describes the situation.
ezekiel68
Hmm, the proliferation of branches, including some which seem perhaps more recent than "v1beta" made me imagine this could apply.
fumeux_fume
I’m sorry have you used Azure? I’ve worked with all the major cloud providers and Google has its warts, but pales in comparison to the hoops Azure make you jump through to make a simple API call.
ic_fly2
Azure API for LLM changes depending on what datacenter you are calling. It is bonkers. In fact it is so bad that at work we are hosting our own LLMs on azure GPU machines rather than use their API. (Which means we only have small models at much higher cost…)
jauntywundrkind
In general, it's just wild to see Google squander such an intense lead.
In 2012, Google was far ahead of the world in making the vast majority of their offerings intensely API-first, intensely API accessible.
It all changed in such a tectonic shift. The Google Plus/Google+ era was this weird new reality where everything Google did had to feed into this social network. But there was nearly no API available to anyone else (short of some very simple posting APIs), where Google flipped a bit, where the whole company stopped caring about the rest of the world and APIs and grew intensely focused on internal use, on themselves, looked only within.
I don't know enough about the LLM situation to comment, but Google squandering such a huge lead, so clearly stopping caring about the world & intertwingularity, becoming so intensely internally focused was such a clear clear clear fall. There's the Google Graveyard of products, but the loss in my mind is more clearly that Google gave up on APIs long ago, and has never performed any clear acts of repentance for such a grevious mis-step against the open world, open possibilities, against closed & internal focus.
simonw
With Gemini 2.5 (both Pro and Flash) Google have regained so much of that lost ground. Those are by far the best long-context models right now, extremely competitively priced and they have features like image mask segmentation that aren't available from other models yet: https://simonwillison.net/2025/Apr/18/gemini-image-segmentat...
jasonfarnon
I think the commenter was saying google squandered its lead ("goodwill" is how I would refer to it) in providing open and interoperable services, not the more recent lead it squandered in AI. I agree with your point that they've made up a lot of that ground with gemini 2.5.
simonw
Yeah you're right, I should have read their comment more closely.
Google's API's have a way steeper learning curve than is necessary. So many of their APIs depend on complex client libraries or technologies like GRPC that aren't used much outside of Google.
Their permission model is diabolically complex to figure out too - same vibes as AWS, Google even used the same IAM acronym.
tyre
Gemini 2.5 Pro is so good. I’ve found that using it as the architect and orchestrator, then farming subtasks and computer use to sonnet, is the best ROI
PantaloonFlames
You can also farm out subtasks to the Gemini Flash models. For example using Aider, use Pro for the "strong" model and Flash for the weak model.
egamirorrim
OOI what's your preferred framework for that managing agent/child agents setup?
candiddevmike
The models are great but the quotas are a real pain in the ass. You will be fighting other customers for capacity if you end up needing to scale. If you have serious Gemini usage in mind, you almost have to have a Google Cloud TAM to advocate for your usage and quotas.
chrisheecho
We have moved our quota system to Dynamic Shared Quota (https://cloud.google.com/vertex-ai/generative-ai/docs/quotas) for 2.0+ models. There are no quotas in DSQ. If you need a guaranteed throughput there is an option to purchase Provisioned Throughput (https://cloud.google.com/vertex-ai/generative-ai/docs/provis...).
harlysparks
Google's headcount (and internal red tap) grew significantly from 2012 to 2025. You're highlighting the fact that at some point in its massive growth, Google had to stop relentlessly pushing R&D and allocate leadership focus on addressing technical debt (or at least operational efficiency) that was a consequence of that growth.
caturopath
I don't understand why Sundar Pichai hasn't been replaced. Google seems like it's been floundering with respect to its ability to innovate and execute in the past decade. To the extent that this Google has been a good maintenance org for their cash cows, even that might not be a good plan if they dropped the ball with AI.
harlysparks
Perhaps you need to first define "innovation" and maybe also rationalize why that view of innovation is the end-all of determining CEO performance. Otherwise you're begging the question here.
Google's stock performance, revenue growth, and political influence in Washington under his leadership has grown substantially. I don't disagree that there are even better CEO's out there, but as an investor, the framing of your question is way off. Given the financial performance, why would you want to replace him?
caturopath
I didn't say that innovation was the end-all of determining CEO performance, though producing new products and creating new markets is the angle that tech tends to go for. I mentioned Google's struggles to execute: they have an astoundingly hard time getting shit done compared to the other largest tech companies.
The counterfactual isn't Google having average performance. You're crediting the stock performance, revenue growth, and political influence (don't really agree this last one was a place Google shined over this period) to Sundar's leadership; I think it has a lot more to do with the company he was handed.
rs186
Answer is simple: he keeps cash coming in and stock price rising. You can compare his performance to his predecessors and CEOs at other companies. That does not necessarily make him a "good" leader in your eyes, but good enough to the board.
huntertwo
Everybody’s thinking the same thing. He sucks.
shawabawa3
Google is the leader in LLMs and self-driving cars, two of the biggest innovation areas in the last decade, so how exactly has it been floundering in its ability to innovate and execute?
caturopath
Google isn't "the leader" in LLMs. Despite a huge funnel to get users in, for intentional use they are a distant second place for consumers, fourth place for LLM APIs, and reputationally treated as an underdog to two tiny companies.
HDThoreaun
googles worth 2 trillion dollars off the back of a website. I think investors are so out of their depth with tech that theyre cool with his mediocre performance
caturopath
Two websites and an ad business.
aaronbrethorst
Hubris. It seems similar, at least externally, to what happened at Microsoft in the late 90s/early 00s. I am convinced that a split-up of Microsoft would have been invigorating for the spin-offs, and the tech industry in general would have been better for it.
Maybe we’ll get a do-over with Google.
sawyna
My personal daily experience with this! I first used vertexai APIs because that's what they suggested, that Gemini APIs are not for production use.
Then there comes the Google.generativeai. I don't remember the reason but they were pushing me to start using this library.
Now it's all flashy google.genai libraries that they are pushing!
I have figured that this is what I should use and this is the documentation that I should look for, because doing a Google search or using an LLM gives me so many confusing results. The only thing that works for sure is reading the library code. That's what I'm doing these days.
For example, the documentation in one of those above libraries say that Gemini can read a document from cloud storage if you give it the uri. That doesn't work in google.genai library. I couldn't figure out why. I imagined maybe Gemini might need access to the cloud storage bucket, but I couldn't find any documentation as to how I can do that. I finally understood that I need to use the new file API and that uri works.
Yes, I like Gemini model they are really good. But the library documentation can be significantly simpler.
msp26
The linked blog is down. But agreed, I would especially like to see this particular thing fixed.
> Property ordering
> When you're working with JSON schemas in the Gemini API, the order of properties is important. By default, the API orders properties alphabetically and does not preserve the order in which the properties are defined (although the Google Gen Al SDKs may preserve this order). If you're providing examples to the model with a schema configured, and the property ordering of the examples is not consistent with the property ordering of the schema, the output could be rambling or unexpected.
SmellTheGlove
Google’s APIs are all kind of challenging to ramp up on. I’m not sure if it’s the API itself or the docs just feeling really fragmented. It’s hard to find what you’re looking for even if you use their own search engine.
PantaloonFlames
The problem I've had is not that the APIs are complicated but that there are so darn many of them.
I agree the API docs are not high on the usability scale. No examples, just reference information with pointers to types, which embed other types, which use abstract descriptions. Figuring out what sort of json payload you need to send, can take...a bunch of effort.
candiddevmike
The Google Cloud API library is meant to be pretty dead simple. While there are bugs, there's a good chance if something's not working it's because of overthinking or providing too many args. Alternatively, doing more advanced stuff and straying from the happy path may lead to dragons.
arccy
they're usually pretty well structured and actually follow design principles like https://cloud.google.com/apis/design and https://google.aip.dev/1
once it clicks, it's infinitely better than the AWS style GetAnythingGoes apis....
miki123211
TBH, my biggest gripe with Google is that they seem to support a slightly different JSON schema format for structured outputs than everybody else. Where Open AI encourages (or even forces) you to use refs for embedding one object in another, Google wants you to embed directly, which is not only wasteful but incompatible with how libraries that abstract over model providers do it.
My structured output code (which uses litellm under the hood, which converts from Pydantic models to JSON schemas), does not work with Google's models for that reason.
intalentive
I used Gemini to write a function that recursively resolves all the refs. Not a big deal to convert your pydantic schemas.
I still don't really understand what Vertex AI is.
If you can ignore Vertex most of the complaints here are solved - the non-Vertex APIs have easy to use API keys, a great debugging tool (https://aistudio.google.com), a well documented HTTP API and good client libraries too.
I actually use their HTTP API directly (with the ijson streaming JSON parser for Python) and the code is reasonably straight-forward: https://github.com/simonw/llm-gemini/blob/61a97766ff0873936a...
You have to be very careful when searching (using Google, haha) that you don't accidentally end up in the Vertext documentation though.
Worth noting that Gemini does now have an OpenAI-compatible API endpoint which makes it very easy to switch apps that use an OpenAI client library over to backing against Gemini instead: https://ai.google.dev/gemini-api/docs/openai
Anthropic have the same feature now as well: https://docs.anthropic.com/en/api/openai-sdk